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How to Register For Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution?
Introduction to AI-102: Designing and Implementing an Azure AI Solution Exam
Candidates for AI-102 Exam are seeking to prove fundamental knowledge and skills in Designing and Implementing an Azure AI Solution domain. Before taking this exam, aspirants ought to have a solid fundamental information of the concepts shared in preparation guide as well as basic understanding of Azure administration, Azure development, and DevOpss would give an added edge.
This exam validates the ability to use the various services within the Microsoft Azure Artificial Intelligence (AI) portfolio.
It is suggested that professionals accustomed to the ideas and also the technologies represented here by taking relevant training courses. Candidates are expected to have some hands-on experience on bot services that use Language Understanding , bots with Azure Application Insights, creating a GPU, FPGA, or CPU-based solution, implementing AI workflow.
After passing this exam, candidates get a certificate from Microsoft that helps them to demonstrate their proficiency to their clients and employers.
Exam Details
Speaking of the exam details, the test will contain from 40 to 60 questions of various types which you need to complete within 100 or 120 minutes (depends on the inclusion of labs). To pass the exam you need to schedule the test on the PearsonVUE platform, pay an exam fee which is currently $165, and score at least 700 points or more out of 1000. And, of course, the knowledge of exam topics is a must.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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Microsoft AI-102日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement computer vision solutions | 10-15% | - Extract text and handwriting from images - Build and deploy custom vision models - Process and index video content - Integrate vision capabilities into applications - Analyze images and detect objects/features |
| Implement natural language processing solutions | 15-20% | - Build conversational AI and chatbots - Implement translation and summarization - Customize and deploy NLP models - Perform text analysis, sentiment detection, and language detection |
| Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback - Deploy and manage generative models |
| Plan and manage an Azure AI solution | 20-25% | - Plan solutions aligned with responsible AI principles - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources - Select appropriate Microsoft Foundry Services - Select suitable AI models - Monitor, optimize, and secure AI solutions |
| Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service |
| Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Implement intelligent search and retrieval - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes |
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